A High Performance Content Based Recommender System Using Hypernym Expansion
Software
·
OSTI ID:1324401
There are two major limitations in content-based recommender systems, the first is accurately measuring the similarity of preferred documents to a large set of general documents, and the second is over-specialization which limits the "interesting" documents recommended from a general document set. To address these issues, we propose combining linguistic methods and term frequency methods to improve overall performance and recommendation.
- Short Name / Acronym:
- Hypernym; 004919WKSTN00
- Version:
- 00
- Programming Language(s):
- Medium: X; OS: LINUX
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- Contributing Organization:
- Thomas E. Potok and Robert M. Patton
- DOE Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1324401
- Country of Origin:
- United States
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